Fangzhu Ai
Biographic Data
| ID | 7146022 |
|---|---|
| NAME | Fangzhu Ai |
| GIVEN NAMES | Fangzhu |
| FAMILY NAME | Ai |
| SIGNATURE | AI F |
| AFFILIATIONS | Jinzhou Medical University |
| ORCID | 0009-0004-1615-1433 |
| VERIFIED | Yes |
| TOTAL WORKS | 6 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 6 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2024 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Internally Validated Logistic Regression Nomogram for Depressive Symptoms Risk Prediction in Middle-Aged and Older Adults With Sarcopenia: Cross-Sectional Study
Sarcopenia is associated with an elevated burden of depressive symptoms, yet screening tools may have limited accuracy and generalizability in this population. We developed and validated an interpretable machine-learning model to predict depressive symptoms risk among middle-aged and older adults with sarcopenia using National Health and Nutrition Examination Survey (NHANES) 2007-2020 data. In this cross-sectional study, we included 913 participa…
A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: A cross-sectional study
Objective: Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model f…
Association between disability and cognitive function in older Chinese people: A moderated mediation of social relationships and depressive symptoms
Objective: Many previous studies have found that disability leads to cognitive impairment, and in order to better understand the underlying mechanisms between disability and cognitive impairment, the present study aimed to investigate the moderating role of social relationships, including their role as mediators between disability and cognitive impairment in depressive symptoms. Study design: This is a cross-sectional study. Methods: A total of 5…
Latent profile analysis of depression in US adults with obstructive sleep apnea hypopnea syndrome
Depression in subjects with OSAHS symptoms can be divided into low-level, moderate-level and high-level depression. There are significant differences among different levels of depression in gender, marital status, PIR, BMI, smoking, general health condition, sleep duration and OSAHS symptom severity
Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly hypertensive people in China: A longitudinal study
The prediction model based on machine learning can accurately assess the likelihood of depression in middle-aged and elderly patients with hypertension in the next three years. And by combining Logistic regression and nomograms, we were able to provide a clear interpretation of personalized risk predictions
Chinese version of the Physical Resilience Scale (PRS): Reliability and validity test based on Rasch analysis
The Physical Resilience Scale has good reliability and is suitable for the assessment of physical resilience tests in older people. However, the overall difficulty of the scale is not suitable for older adults of all ability ranges, and it is possible to add higher and lower difficulty items and adjust the difficulty spacing between items in a later study
No prominent works on this page.
A machine learning model to predict the risk of depression in US adults with obstructive sleep apnea hypopnea syndrome: A cross-sectional study
Objective: Depression is very common and harmful in patients with obstructive sleep apnea hypopnea syndrome (OSAHS). It is necessary to screen OSAHS patients for depression early. However, there are no validated tools to assess the likelihood of depression in patients with OSAHS. This study used data from the National Health and Nutrition Examination Survey (NHANES) database and machine learning (ML) methods to construct a risk prediction model f…
Association between disability and cognitive function in older Chinese people: A moderated mediation of social relationships and depressive symptoms
Objective: Many previous studies have found that disability leads to cognitive impairment, and in order to better understand the underlying mechanisms between disability and cognitive impairment, the present study aimed to investigate the moderating role of social relationships, including their role as mediators between disability and cognitive impairment in depressive symptoms. Study design: This is a cross-sectional study. Methods: A total of 5…
Latent profile analysis of depression in US adults with obstructive sleep apnea hypopnea syndrome
Depression in subjects with OSAHS symptoms can be divided into low-level, moderate-level and high-level depression. There are significant differences among different levels of depression in gender, marital status, PIR, BMI, smoking, general health condition, sleep duration and OSAHS symptom severity
Construction of a machine learning-based risk prediction model for depression in middle-aged and elderly hypertensive people in China: A longitudinal study
The prediction model based on machine learning can accurately assess the likelihood of depression in middle-aged and elderly patients with hypertension in the next three years. And by combining Logistic regression and nomograms, we were able to provide a clear interpretation of personalized risk predictions
Chinese version of the Physical Resilience Scale (PRS): Reliability and validity test based on Rasch analysis
The Physical Resilience Scale has good reliability and is suitable for the assessment of physical resilience tests in older people. However, the overall difficulty of the scale is not suitable for older adults of all ability ranges, and it is possible to add higher and lower difficulty items and adjust the difficulty spacing between items in a later study
Internally Validated Logistic Regression Nomogram for Depressive Symptoms Risk Prediction in Middle-Aged and Older Adults With Sarcopenia: Cross-Sectional Study
Sarcopenia is associated with an elevated burden of depressive symptoms, yet screening tools may have limited accuracy and generalizability in this population. We developed and validated an interpretable machine-learning model to predict depressive symptoms risk among middle-aged and older adults with sarcopenia using National Health and Nutrition Examination Survey (NHANES) 2007-2020 data. In this cross-sectional study, we included 913 participa…
Medicine (4 works) · Computer Science (3 works) · Depression (economics (3 works) · Internal Medicine (3 works) · Logistic regression (3 works) · Nomogram (3 works) · Psychiatry (3 works) · Psychology (3 works) · Apnea (2 works) · Clinical Psychology (2 works)